初始化换发型项目：3个微服务代码 + 部署脚本

包含：
- hair_service_sd: 换发型/换发色算法服务 (端口 8801)
- photo_service: LoRA 训练调度服务 (端口 32678)
- stable-diffusion-webui: SD WebUI 推理服务 (端口 57860)
- kohya_ss_home: 训练环境代码
- meidaojia: 监控测试脚本
- setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查)
- start_all_services.sh: 启动3个服务
- configure.ini.template: 路径模板化 (BASE_DIR自动推导)
- conda_envs/py310.yml: py310 环境定义

大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除，
由网盘单独上传。

hair_service_sd/momocv/LeftEye.py

@@ -0,0 +1,94 @@
+import sys
+import os
+import cv2
+import torch
+import torch.nn as nn
+
+def op_name(op_name, m):
+    m.op_name = op_name
+    return m
+
+def conv_bn_relu(name, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, momentum = 0.1, track_running_stats=True):
+    return nn.Sequential(
+        op_name(name, nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, False)),
+        op_name(name + '/bn', nn.BatchNorm2d(out_channels, momentum = momentum, track_running_stats = track_running_stats)),
+        op_name(name + '/relu', nn.ReLU(inplace=True)),
+    )
+
+
+# Define a Basic resnet block
+class BasicResnetBlock(nn.Module):
+    def __init__(self, name, in_channels, out_channels, kernel_size=3, stride=2, padding=1, direct_plus=False):
+        super(BasicResnetBlock, self).__init__()
+
+        self.op_name = name
+
+        self.conv_block = nn.Sequential(
+            conv_bn_relu(name=name + '/conv1', in_channels=in_channels, out_channels=out_channels,
+                         kernel_size=kernel_size, stride=stride, padding=padding),
+
+            conv_bn_relu(name=name + '/conv2', in_channels=out_channels, out_channels=out_channels,
+                         kernel_size=3, stride=1, padding=1)
+        )
+
+        if direct_plus and (in_channels == out_channels) and (stride == 1):
+            self.sc_block = None
+        else:
+            self.sc_block = conv_bn_relu(name=name + '/sc_conv', in_channels=in_channels, out_channels=out_channels,
+                                         kernel_size=1, stride=stride, padding=0)
+
+    def forward(self, x):
+        if self.sc_block is None:
+            out = x + self.conv_block(x)
+        else:
+            out = self.conv_block(x) + self.sc_block(x)
+        return out
+
+class Flatten(nn.Module):
+    def __init__(self, axis):
+        super(Flatten, self).__init__()
+        self.axis = axis
+
+    def forward(self, x):
+        assert self.axis == 1
+        x = x.reshape(x.shape[0], -1)
+        # x = x.view(-1, 1152)
+        return x
+
+def flatten(name, axis):
+    return op_name(name, Flatten(axis))
+
+def linear_bn_relu(name, in_features, out_features, momentum = 0.1, track_running_stats=True):
+    return nn.Sequential(
+        op_name(name + '/FC', nn.Linear(in_features, out_features)),
+        op_name(name + '/bn', nn.BatchNorm1d(out_features, momentum = momentum, track_running_stats = track_running_stats)),
+        op_name(name + '/relu', nn.ReLU(inplace=True)),
+    )
+
+class LeftEye(nn.Module):
+    def __init__(self, name, in_channels, out_channels):
+        super(LeftEye, self).__init__()
+        self.op_name = name
+
+        op_list = []
+
+        op_list += [conv_bn_relu(name + '/first_conv', in_channels, 24, kernel_size = 5, stride = 2, padding = 2) ]
+
+        ch_num = [24, 32, 64, 96, 128]
+
+        op_list += [BasicResnetBlock(name + '/stage%d'%(i + 1), ch_num[i], ch_num[i+1]) for i in range(len(ch_num) - 1) ]
+
+        op_list += [flatten(name + '/flatten', 1),
+                    linear_bn_relu(name + '/FC1', 1152, 256),
+                    op_name(name + '/FC2', nn.Linear(256, out_channels))]
+
+        self.conv_block = nn.Sequential(*op_list)
+
+    def forward(self, x):
+        return self.conv_block(x)
+
+def get_left_eye_symbol(symbol_name='BigResNet', input_nc = 3, output_nc = 17 * 2):
+    return LeftEye(symbol_name, input_nc, output_nc)
+
+if __name__=='__main__':
+    pass